Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network

Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network

In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However,...

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Bibliographic Details
Main Authors: Gihyoun Lee, Sang Hyeon Jin, Jinung An
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Sensors
Subjects:
functional near-infrared spectroscopy
motion artifact
artificial neural network
signal entropy
wavelet transform
Online Access:http://www.mdpi.com/1424-8220/18/9/2957

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